8
1 Introduction
rials Modelling Council (EMMC) as an interest group and community organization;
the second, OntoCommons, supports the uptake of ontologies as a technology in
materials modelling. The associated interoperability effort is based on the Review
of Materials Modelling (RoMM), a compendium that aims at establishing a coherent understanding of all major modelling and simulation approaches from quantum mechanics up to continuum methods [43]. On this basis, first, MODA (Model
Data) was introduced as a standardized description simulation workflows together
with their intended use cases [44]; subsequently, a variety of domain ontologies
were developed and connected to the European Materials and Modelling Ontology
(EMMO), a top-level ontology aiming at describing all that exists from a perspective
that is advantageous for CME/ICME infrastructures and applications [45, 46]. EngMeta, on the other hand, was developed at the University of Stuttgart in an environment where digitalization of materials modelling is advanced through the Cluster of
Excellence “Data-Integrated Simulation Science” (EXC 2075), the Stuttgart Center
for Simulation Science, and work on repositories including ReSUS (Reusable Software University Stuttgart), DaRUS (Data Repository of the University of Stuttgart),
and the programme for national research data infrastructures (NFDI) of the German
Research Foundation (DFG).
Both approaches have their strengths. On the one hand, ontologies are experiencing a great surge in popularity. They are increasingly seen as a key component
of state-of-the-art solutions in data technology. Nonetheless, they also have drawbacks. First, as a technology, ontologies are comparably heavy, requiring substantial
resources for development and maintenance. While certain communities have succeeded at establishing agreed domain-specific semantic frameworks [47, 48], it is
also commonly found that (occasionally quite complex) ontologies are developed
within a project and then abandoned when the project is over. While it is undeniable
that classification schemes, in general, are a prerequisite for interoperability, there
is no consensus on whether a less expressive framework or a more expressive one
is to be preferred. The advantage of less expressive languages is they can be handled with multiple technologies and tools, and are typically lighter and faster. Richer
languages allow to describe more complex relations, but at the price of being tied
to newer, less widespread technologies and being typically computationally more
demanding. Moreover, all new technologies need to overcome a barrier to adoption
before they are employed widely. Superficially, it may seem that ontologies have
advanced relatively far on this path; however, comparing the uptake of ontologybased semantic technologies in research software engineering (simulation codes,
etc. ) with alternatives such as XML/XSD-based solutions makes it clear that the
advance of ontology-based solutions is still at an early stage.
Additionally, ontology design can result in an overregulation of domain practices.
This risk is inherent to the prescriptive (rather than descriptive) nature of ontologies and taxonomies—like grammar regulates syntax, ontologies pretend to regulate
meaning. In reality, however, there are always many possible ways to ontologize any
given domain of knowledge. The concurrent development of multiple incommensurable paradigms is one of the manifestations of progress in a scientific discipline
and the major driving force for the emergence of new specializations [49]; in the
1 Introduction
rials Modelling Council (EMMC) as an interest group and community organization;
the second, OntoCommons, supports the uptake of ontologies as a technology in
materials modelling. The associated interoperability effort is based on the Review
of Materials Modelling (RoMM), a compendium that aims at establishing a coherent understanding of all major modelling and simulation approaches from quantum mechanics up to continuum methods [43]. On this basis, first, MODA (Model
Data) was introduced as a standardized description simulation workflows together
with their intended use cases [44]; subsequently, a variety of domain ontologies
were developed and connected to the European Materials and Modelling Ontology
(EMMO), a top-level ontology aiming at describing all that exists from a perspective
that is advantageous for CME/ICME infrastructures and applications [45, 46]. EngMeta, on the other hand, was developed at the University of Stuttgart in an environment where digitalization of materials modelling is advanced through the Cluster of
Excellence “Data-Integrated Simulation Science” (EXC 2075), the Stuttgart Center
for Simulation Science, and work on repositories including ReSUS (Reusable Software University Stuttgart), DaRUS (Data Repository of the University of Stuttgart),
and the programme for national research data infrastructures (NFDI) of the German
Research Foundation (DFG).
Both approaches have their strengths. On the one hand, ontologies are experiencing a great surge in popularity. They are increasingly seen as a key component
of state-of-the-art solutions in data technology. Nonetheless, they also have drawbacks. First, as a technology, ontologies are comparably heavy, requiring substantial
resources for development and maintenance. While certain communities have succeeded at establishing agreed domain-specific semantic frameworks [47, 48], it is
also commonly found that (occasionally quite complex) ontologies are developed
within a project and then abandoned when the project is over. While it is undeniable
that classification schemes, in general, are a prerequisite for interoperability, there
is no consensus on whether a less expressive framework or a more expressive one
is to be preferred. The advantage of less expressive languages is they can be handled with multiple technologies and tools, and are typically lighter and faster. Richer
languages allow to describe more complex relations, but at the price of being tied
to newer, less widespread technologies and being typically computationally more
demanding. Moreover, all new technologies need to overcome a barrier to adoption
before they are employed widely. Superficially, it may seem that ontologies have
advanced relatively far on this path; however, comparing the uptake of ontologybased semantic technologies in research software engineering (simulation codes,
etc. ) with alternatives such as XML/XSD-based solutions makes it clear that the
advance of ontology-based solutions is still at an early stage.
Additionally, ontology design can result in an overregulation of domain practices.
This risk is inherent to the prescriptive (rather than descriptive) nature of ontologies and taxonomies—like grammar regulates syntax, ontologies pretend to regulate
meaning. In reality, however, there are always many possible ways to ontologize any
given domain of knowledge. The concurrent development of multiple incommensurable paradigms is one of the manifestations of progress in a scientific discipline
and the major driving force for the emergence of new specializations [49]; in the